Dummy substrate detection method, dummy substrate detector device and system

ABSTRACT

A dummy substrate detection method includes: acquiring a real-time image of a conveying route when a cut substrate enters the conveying route; matching and comparing the acquired real-time image of the conveying route with a first reference image, and determining whether or not there is a dummy substrate on the conveying route according to a comparison result. The first reference image is an image of the conveying route with no dummy substrate. A dummy substrate detector device and a dummy substrate detector system are further provided.

The present application claims priority of Chinese Patent ApplicationNo. 201710335385.0 filed on May 12, 2017, the disclosure of which isincorporated herein by reference in its entirety as part of the presentapplication.

TECHNICAL FIELD

Embodiments of the present disclosure relate to a dummy substratedetection method, a dummy substrate detector device, and a dummysubstrate detector system.

BACKGROUND

In a TFT-LCD industry, in order to divide a mother glass substrate intoa plurality of glass panels of a desired size after a cell assemblingprocess, it is necessary to cut the mother glass substrate. During aprocedure of cutting the mother glass substrate, a large amount of dummyglass, which are not needed by the glass panels, are generated.

Development of the cutting technology has experienced from a single-sidecutting process to a double-side cutting process. For example, thecutting process for a low-generation production line (<G6) or a Qproduct is performed in a mode of array substrate (TFT substrate)cutting→array substrate (TFT substrate) splitting→color filter substrate(CF substrate) cutting→color filter substrate (CF substrate)splitting→taking out, or in a mode of array substrate (TFT substrate)cutting→color filter substrate (CF substrate) cutting→splitting→takingout. Apparatus for implementing the cutting process may adopt an inlinefully-automatically-controlled mode so that the dummy glass is detectedby using a vacuum adsorption method, or a semi-auto mode so that thedummy glass is detected and removed manually. For example, as for ahigh-generation production line (>G6), the mother glass substrate is cutby the double-side (bottom side and top side) cutting process withfactors such as cutting efficiency taken into consideration, and theapparatus for implementing the cutting process mainly adopts thefully-automatically-controlled inline mode.

Regardless of which of the above-described cutting apparatus is adopted,as long as the cutting apparatus is operated in the inlinefully-automatically-controlled mode, a process of taking out the glasspanel is that: the glass panel is taken away by a pickup hand andconveyed to a next procedure by a conveyor, and the remaining dummyglass flows into a waste glass recovery procedure. In order to preventthe dummy glass from entering the downstream procedure along with theglass panel, which causes defects such as scratch of a surface of theglass panel and damage to an edge of the glass panel, presence orabsence of the dummy glass for example are detected in two modes below:one is adding a dummy glass detector element to the cutting apparatus,for example, dummy Y and dummy X elements of an MDI apparatus, that is,determining whether or not the edge of the glass panel carries with thedummy glass by combining a stepper motor with a detecting pin andmonitoring a torque (torsion) value of the stepper motor; the other isadding an vacuum adsorption element, that is, determining whether or notthere is the dummy glass carried by the glass panel according to avacuum value.

However, in an actual production process, it is found that: any one ofthe above-described detecting modes may only be able to detect the dummyglass regularly arranged at the edge of the glass panel, but not be ableto detect the dummy glass dropped in a path of the conveyor (i.e. aconveying route). In an automatic production mode, a case where thedummy glass is dropped in the conveying route is prone to occur on theway of the glass panel entering the downstream apparatus; in addition,tact time of a single product is very short, so once the case where thedummy glass is dropped in the conveying route and is not found out intime occurs, a scratch defect may be caused to a large number ofsubsequent glass panels in a very short time, which thus will causesignificant loss to a production company.

SUMMARY

According to embodiments of the disclosure, a dummy substrate detectionmethod, a dummy substrate detector device, and a dummy substratedetector system are provided.

According to the embodiments of the disclosure, the dummy substratedetection method is provided. The method comprises: acquiring areal-time image of a conveying route when a cut substrate enters theconveying route; matching and comparing the acquired real-time image ofthe conveying route with a first reference image, and determiningwhether or not there is a dummy substrate on the conveying routeaccording to a comparison result; wherein, the first reference image isan image of the conveying route with no dummy substrate.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the matching and comparing the acquiredreal-time image of the conveying route with the first reference image,and determining whether or not there is the dummy substrate on theconveying route according to the comparison result, includes:

performing grayscale processing on the first reference image, togenerate a pixel grayscale value matrix of the first reference image:

$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the first reference image; and G¹(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first reference image;

defining the acquired real-time image of the conveying route as a firstreal-time image, and performing grayscale processing on the firstreal-time image, to generate a pixel grayscale value matrix of the firstreal-time image:

$H_{x\gamma}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the first real-time image; and H¹(x, y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first real-time image;

intercepting an M*N sub-matrix G_(MIN) from the pixel grayscale valuematrix of the first reference image, and defining the sub-matrix as afirst reference sub-matrix;

obtaining an M*N sub-matrix matching the first reference sub-matrix fromthe pixel grayscale value matrix of the first real-time image, anddefining the sub-matrix as a first real-time sub-matrix; and

performing error analysis between the first real-time sub-matrix and thefirst reference sub-matrix, and determining whether or not there is thedummy substrate on the conveying route according to an error analysisresult.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the obtaining the M*N sub-matrix matchingthe first reference sub-matrix from the pixel grayscale value matrix ofthe first real-time image, includes:

setting an M*N sub-matrix H_(MN) ^(m,n) intercepted with an m-th row andan n-th column as starting position from the pixel grayscale valuematrix of the first real-time image as the M*N sub-matrix matching thefirst reference sub-matrix B_(MN), where, 1≤m+M≤x, 1≤n+N≤y;

defining a first matching function:

$D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\underset{j = 1}{\sum\limits^{N}}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}$

where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is an element of ani-th row and a j-th column in the matrix G_(MN);

defining a first correlation function:

${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$

calculating numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtaining the M*N sub-matrixH_(MN) ^(m,n) matching the first reference sub-matrix according to thenumerical values of m and n.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the performing error analysis between thefirst real-time sub-matrix and the first reference sub-matrix, anddetermining whether or not there is the dummy substrate on the conveyingroute according to the error analysis result, includes:

defining a first single pixel absolute error:

ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)− H}−{G _(MN)(i, j)− G}|

where,

${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$

defining a first objective function,

${E_{1} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{1}\left( {i,j} \right)}}}};$

setting a first objective function threshold K¹, and comparing the firstobjective function with the first objective function threshold K¹;

determining that there is the dummy substrate on the conveying route, ifthe first objective function is greater than the first objectivefunction threshold K¹;

determining that there is no dummy substrate on the conveying route, ifthe first objective function is not greater than the first objectivefunction threshold K¹.

For example, the dummy substrate detection method according to theembodiments of the disclosure further comprises: acquiring a real-timeimage during a process of the cut substrate passing through theconveying route; matching and comparing the acquired real-time imageduring the process of the cut substrate passing through the conveyingroute with a second reference image, and determining whether or notthere is the dummy substrate on the cut substrate according to acomparison result; wherein, the second reference image is an imageduring a process of the cut substrate with no dummy substrate passingthrough the conveying route.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the matching and comparing the acquiredreal-time image during the process of the cut substrate passing throughthe conveying route with the second reference image, and determiningwhether or not there is the dummy substrate on the cut substrateaccording to the comparison result, includes steps of:

performing grayscale processing on the second reference image, togenerate a pixel grayscale value matrix of the second reference image:

$G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the second reference image; and G²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second referenceimage;

defining the acquired real-time image during the cut substrate passingthrough the conveying route as a second real-time image, and performinggrayscale processing on the second real-time image, to generate a pixelgrayscale value matrix of the second real-time image:

$H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the second real-time image; and H²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second real-timeimage;

intercepting an E*F sub-matrix G_(EF) from the pixel grayscale valuematrix of the second reference image, and defining the sub-matrix as asecond reference sub-matrix;

obtaining an E*F sub-matrix matching the second reference sub-matrixfrom the pixel grayscale value matrix of the second real-time image, anddefining the sub-matrix as a second real-time sub-matrix;

performing error analysis between the second real-time sub-matrix andthe second reference sub-matrix, and determining whether or not there isthe dummy substrate on the cut substrate according to an error analysisresult.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the obtaining the E*F sub-matrix matchingthe second reference sub-matrix from the pixel grayscale value matrix ofthe second real-time image, includes:

setting an E*F sub-matrix H_(EF) ^(e,f) intercepted with an e-th row andan f-th column as starting position from the pixel grayscale valuematrix of the second real-time image as the E*F sub-matrix matching thesecond reference sub-matrix, where, 1≤e+E≤x, 1≤f+F≤y;

defining a second matching function:

${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) is an element of ani-th row and a j-th column in the matrix G_(EF);

defining a second correlation function:

${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$

calculating numerical values of e and f when the second correlationfunction R_((e, f)) is closest to 1, and obtaining the E*F sub-matrixH_(EF) ^(e,f) matching the second reference sub-matrix, according to thenumerical values of e and f.

For example, in the dummy substrate detection method according to theembodiments of the disclosure, the performing error analysis between thesecond real-time sub-matrix and the second reference sub-matrix, anddetermining whether or not there is the dummy substrate on the cutsubstrate according to the error analysis result, includes:

defining a second single pixel absolute error:

ε²(i, j)=|{H _(EF) ^(e,f)(i, j)−H}−{G_(EF)(i, j)−G}|;

where,

${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};{\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$

defining a second objective function,

${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$

setting a second objective function threshold K², and comparing thesecond objective function with the second objective function thresholdK²;

determining that there is the dummy substrate on the cut substrate, ifthe second objective function is greater than the second objectivefunction threshold K²;

determining that there is no dummy substrate on the cut substrate, ifthe second objective function is not greater than the second objectivefunction threshold K².

According to the embodiments of the disclosure, the dummy substratedetector device is provided. The device comprises: an image acquisitioncomponent, configured to acquire a real-time image of a conveying routewhen a cut substrate enters the conveying route, and save the image as afirst real-time image; an image processing component, configured tomatch and compare the first real-time image with a first referenceimage, and determine whether or not there is a dummy substrate on theconveying route according to a comparison result; wherein, the firstreference image is an image of the conveying route with no dummysubstrate.

For example, in the dummy substrate detector device according to theembodiments of the disclosure, the image processing component includes:

a matrix processor element, configured to:

perform grayscale processing on the first reference image, to generate apixel grayscale value matrix of the first reference image:

$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the first reference image; and G¹(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first reference image;

perform grayscale processing on the first real-time image, to generate apixel grayscale value matrix of the first real-time image:

$H_{x\gamma}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the first real-time image; and H¹(x, y) is a grayscale value ofthe pixel located in an x-th row and a y-th column in the firstreal-time image;

a matrix matcher element, configured to:

intercept an M*N sub-matrix G_(MN) from the pixel grayscale value matrixof the first reference image, and define the sub-matrix as a firstreference sub-matrix;

obtain an M*N sub-matrix matching the first reference sub-matrix fromthe pixel grayscale value matrix of the first real-time image, anddefine the sub-matrix as a first real-time sub-matrix;

an analyzer element, configured to:

perform error analysis between the first real-time sub-matrix and thefirst reference sub-matrix, and determine whether or not there is thedummy substrate on the conveying route according to an error analysisresult.

For example, in the dummy substrate detector device according to theembodiments of the disclosure, the matrix matcher element is configuredto:

set an M*N sub-matrix H_(MN) ^(m,n) intercepted with an m-th row and ann-th column as starting position from the pixel grayscale value matrixof the first real-time image as the M*N sub-matrix matching the firstreference sub-matrix G_(MN), where, 1≤m+M≤x, 1≤n+N≤y;

define a first matching function:

${D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\underset{j = 1}{\sum\limits^{N}}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is an element of ani-th row and a j-th column in the matrix G_(MN);

define a first correlation function:

${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$

calculate numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtain the M*N sub-matrix H_(MN)^(n,n) matching the first reference sub-matrix according to thenumerical values of m and n;

the analyzer element, configured to:

define a first single pixel absolute error:

ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)− H}″{G _(MN)(i, j)− G}|;

where,

${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$

define a first objective function,

$E_{1} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{1}\left( {i,j} \right)}}}$

set a first objective function threshold K¹, and compare the firstobjective function with the first objective function threshold K¹;

determine that there is the dummy substrate on the conveying route, ifthe first objective function is greater than the first objectivefunction threshold K¹;

determine that there is no dummy substrate on the conveying route, ifthe first objective function is not greater than the first objectivefunction threshold K¹.

For example, in the dummy substrate detector device according to theembodiments of the disclosure, the image acquisition component isfurther configured to acquire a real-time image during a process of thecut substrate passing through the conveying route, and save the image asa second real-time image; the image processing component is furtherconfigured to match and compare the second real-time image with a secondreference image, and determine whether or not there is the dummysubstrate on the cut substrate according to a comparison result;wherein, the second reference image is an image during the process ofthe cut substrate with no dummy substrate passing through the conveyingroute.

For example, in the dummy substrate detector device according to theembodiments of the disclosure, in the image processing component,

the matrix processor element, is further configured to:

perform grayscale processing on the second reference image, to generatea pixel grayscale value matrix of the second reference image:

$G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the second reference image; and G²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second referenceimage;

perform grayscale processing on the second real-time image, to generatea pixel grayscale value matrix of the second real-time image:

$H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$

where, x and y are respectively a row number and a column number of apixel in the second real-time image; and H²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second real-timeimage;

the matrix matcher element is further configured to:

intercept an E*F sub-matrix G_(EF) from the pixel grayscale value matrixof the second reference image; and define the sub-matrix as a secondreference sub-matrix;

obtain an E*F sub-matrix matching the second reference sub-matrix fromthe pixel grayscale value matrix of the second real-time image, anddefine the sub-matrix as a second real-time sub-matrix;

the analyzer element is further configured to:

perform error analysis between the second real-time sub-matrix and thesecond reference sub-matrix, and determine whether or not there is thedummy substrate on the cut substrate according to an error analysisresult.

For example, in the dummy substrate detector device according to theembodiments of the disclosure, the matrix matcher element is configuredto:

set the E*F sub-matrix H_(G) _(EF) ^(e,f) intercepted with an e-th rowand an f-th column as starting position from the pixel grayscale valuematrix of the second real-time image as the E*F sub-matrix matching thesecond reference sub-matrix G_(EF), where, 1≤e+E≤x, 1≤f+F≤y;

define a second matching function:

${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) an element of ani-th row and a j-th column in the matrix G_(EF);

define a second correlation function:

${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$

calculate numerical values of e and f when the second correlationfunction R_((e, f)) is closest to 1, and obtain the E*F sub-matrixH_(EF) ^(e,f) matching the second reference sub-matrix, according to thenumerical values of e and f;

the analyzer element is configured to:

define a second single pixel absolute error

ε²(i, j)=|{H _(EF) ^(e,f)(i, j)− H}−{G _(EF)(i, j)− G}|

where,

${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};}$

define a second objective function,

${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$

set a second objective function threshold K² and compare the secondobjective function with the second objective function threshold K²;

determine that there is the dummy substrate on the cut substrate, if thesecond objective function is greater than the second objective functionthreshold K²;

determine that there is no dummy substrate on the cut substrate, if thesecond objective function is not greater than the second objectivefunction threshold K².

According to the embodiments of the disclosure, the dummy substratedetector system is provided. The method comprises the dummy substratedetector device as described above; and, a conveyor device, configuredto convey the cut substrate through the conveying route; a driverdevice, configured to drive the conveyor device to move; a first sensor;at a starting end of the conveying route, configured to sense the cutsubstrate and generate a first sensing signal; a main controller device,in signal connection with the image acquisition component, the imageprocessing component, the driver device and the first sensor, configuredto: control the driver device to stop driving the conveyor device, whenreceiving the first sensing signal of the first sensor, and control theimage acquisition component to acquire the real-time image of theconveying route; and, control the driver device to drive the conveyordevice to continue to move, in a case where the image processingcomponent determines that there is no dummy substrate on the conveyingroute.

For example, the dummy substrate detector system according to theembodiments of the disclosure further comprises: a second sensor insignal connection with the main controller device, wherein, the secondsensor is in the conveying route, and is configured to sense the cutsubstrate and generate a sensing signal; the main controller device,further configured to: control the image acquisition component toacquire the real-time image during the process of the cut substratesensed by the second sensor passing through the conveying route, whenreceiving the second sensing signal of the second sensor; and generatean alarm signal, in a case where the image processing componentdetermines that there is the dummy substrate on the cut substrate.

For example, in the dummy substrate detector system according to theembodiments of the disclosure, the image acquisition component includesa one-dimensional linear image sensor and a servo driver configured todrive the one-dimensional linear image sensor to move so as to obtain animage.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to clearly illustrate the technical solution of the embodimentsof the disclosure, the drawings of the embodiments will be brieflydescribed in the following; it is obvious that the described drawingsare only related to some embodiments of the disclosure and thus are notlimitative of the disclosure.

FIG. 1 is a flow chart of a dummy substrate detection method provided byembodiments of the present disclosure;

FIG. 2 is a flow chart of step S103 in the dummy substrate detectionmethod in FIG. 1;

FIG. 3 is a flow chart of step S106 in the dummy substrate detectionmethod in FIG. 1;

FIG. 4 is a structural block diagram of a dummy substrate detectorsystem provided by the embodiments of the present disclosure;

FIG. 5 is a structural block diagram of an image processing component ina dummy substrate detector device provided by the embodiments of thepresent disclosure;

FIG. 6 is a structural block diagram of an image acquisition componentin the dummy substrate detector device provided by the embodiments ofthe present disclosure;

FIG. 7 is a top-view structural schematic diagram of a portion of thedummy substrate detector system provided by the embodiments of thepresent disclosure;

FIG. 8 is a side-view structural schematic diagram of a portion of thedummy substrate detector system provided by the embodiments of thepresent disclosure;

FIG. 9 is a schematic flow chart of the dummy substrate detector systemfor detecting whether or not there is a dummy substrate o a conveyingroute provided by the embodiments of the present disclosure;

FIG. 10 is another schematic flow chart of the dummy substrate detectorsystem for detecting whether or not there is the dummy substrate on theconveying route provided by embodiments of the present disclosure

FIG. 11 is a schematic flow chart of the dummy substrate detector systemfor detecting whether or not there is the dummy substrate on a cutsubstrate provided by the embodiments of the present disclosure; and

FIG. 12 is another schematic flow chart of the dummy substrate detectorsystem for detecting whether or not there is the dummy substrate on thecut substrate provided by embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to make objects, technical details and advantages of theembodiments of the disclosure apparent, the technical solutions of theembodiments will be described in a clearly and fully understandable wayin connection with the drawings related to the embodiments of thedisclosure. It is obvious that the described embodiments are just a partbut not all of the embodiments of the disclosure. Based on the describedembodiments herein, those skilled in the art can obtain otherembodiment(s), without any inventive work, which should be within thescope of the disclosure.

Unless otherwise defined, the technical terms or scientific terms hereshould be of general meaning as understood by those ordinarily skilledin the art. In the descriptions and claims of the present disclosure,expressions such as “first”, “second” and the like do not denote anyorder, quantity, or importance, but rather are used for distinguishingdifferent components. Expressions such as “include” or “comprise” andthe like denote that elements or objects appearing before the words of“include” or “comprise” cover the elements or the objects enumeratedafter the words of “include” or “comprise” or equivalents thereof, notexclusive of other elements or objects. Expressions such as “up”,“down”, “left”, “right” and the like are only used for expressingrelative positional relationship, the relative positional relationshipmay be correspondingly changed in a case where the absolute position ofa described object is changed.

At least one embodiment of the present disclosure provides a dummysubstrate detection method, comprising: acquiring a real-time image of aconveying route when a cut substrate, which has been cut, enters theconveying route; matching and comparing the acquired real-time image ofthe conveying route with a first reference image, and determiningwhether or not there is a dummy substrate on the conveying routeaccording to a comparison result; in which, the first reference image isan image of the conveying route with no dummy substrate. For example,FIG. 1 is a flow chart of the dummy substrate detection method providedby the embodiments of the present disclosure.

As shown in FIG. 1, the dummy substrate detection method comprises:

Step S101: acquiring the real-time image of the conveying route when thecut substrate enters the conveying route;

Step S102: matching and comparing the acquired real-time image of theconveying route with the first reference image, and determining whetheror not there is the dummy substrate on the conveying route according tothe comparison result; in which, the first reference image is the imageof the conveying route with no dummy substrate.

In the above-described dummy substrate detection method, in step S101 tostep S102, the image of the conveying route with no dummy substrate istaken as the first reference image, and the real-time image of theconveying route when the cut substrate enters the conveying route iscompared with the above-described first reference image and analyzed,which accurately and effectively determines whether or not there is thedummy substrate on the conveying route when the cut substrate enters theconveying route, so as to achieve the detection of the dummy substrateon the conveying route. Therefore, by using the above-described dummysubstrate detection method, the dummy substrate dropped in the conveyingroute is effectively detected, so as to prevent the cut substrate whichis to pass through the conveying route from being scratched.

As shown in FIG. 1 and FIG. 2, in the embodiments of the presentdisclosure, step S102, i.e., matching and comparing the acquiredreal-time image of the conveying route with the first reference image,and determining whether or not there is the dummy substrate on theconveying route according to the comparison result, includes steps of:

Step S201:

Performing grayscale processing on the first reference image, togenerate a pixel grayscale value matrix of the first reference image:

$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the first reference image; and G¹(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first reference image;

Defining the acquired real-time image of the conveying route as a firstreal-time image, and performing grayscale processing on the firstreal-time image, to generate a pixel grayscale value matrix of the firstreal-time image:

$H_{xy}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the first real-time image; and H¹(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first real-time image;

Step S202:

Intercepting an M*N sub-matrix G_(MN) from the pixel grayscale valuematrix G_(xy) ¹ of the first reference image, and defining thesub-matrix as a first reference sub-matrix (i.e., a matrix formed bygrayscale values of pixels of a partial region of the first referenceimage);

Obtaining an M*N sub-matrix matching the first reference sub-matrix fromthe pixel grayscale value matrix H_(xy) ¹ of the first real-time image,and defining the sub-matrix as a first real-time sub-matrix (i.e., amatrix formed by grayscale values of pixels of a partial region of thefirst real-time image);

Step S203:

Performing error analysis between the first real-time sub-matrix and thefirst reference sub-matrix, and determining whether or not there is thedummy substrate on the conveying route according to an error analysisresult.

During the image acquisition process, factors such as slight mechanicalvibration may cause a mismatch between the pixels of the first referenceimage and the pixels the first real-time image. In step S102 of thedummy substrate detection method provided by the embodiments of thepresent disclosure, the first reference sub-matrix and the firstreal-time sub-matrix are obtained by matching processing, and then theerror analysis is performed between the first reference sub-matrix andthe first real-time sub-matrix to determine whether or not there is thedummy substrate on the conveying route; and thus the method according tothe embodiments of the disclosure avoids erroneous determination causedby interference of factors such as environment and mechanical stability.

As shown in FIG. 2, on the basis of the above-described embodiments, instep S202, the obtaining the M*N sub-matrix matching the first referencesub-matrix from the pixel grayscale value matrix of the first real-timeimage, for example is implemented in a mode below:

Setting an M*N sub-matrix H_(MN) ^(m,n) intercepted with an m-th row andan n-th column as starting position from the pixel grayscale valuematrix H_(xy) ¹ of the first real-time image as the sub-matrix matchingthe first reference sub-matrix G_(MN), where, 1≤m+M≤x, 1≤n+N≤y,

Defining a first matching function:

${D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\underset{j = 1}{\sum\limits^{N}}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

Where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is an element of ani-th row and a j-th column in the matrix G_(MN);

Defining a first correlation function:

${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$

Calculating numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtaining H_(MN) ^(m,n), thatis, obtaining the M*N sub-matrix matching the first referencesub-matrix, according to the numerical values of m and n.

According to the embodiments of the disclosure, two sub-matrices (i.e.,G_(MN) and H_(MN) ^(m,n)) whose correlation degree is closest to 1 arerespectively extracted from the pixel grayscale value matrix of thefirst reference image and the pixel grayscale value matrix of the firstreal-time image, that is, two sub-images matching each other arerespectively extracted from the first reference image and the firstreal-time image, and the two sub-images are compared to determinewhether or not there is the dummy substrate on the conveying route,which avoids erroneous determination caused by interference of factorssuch as environment and mechanical stability. For example, it isrequired that the two sub-images at least respectively include pixels ofa path portion that the cut substrate is to pass on the conveying route,so as to ensure that whether or not there is the dummy substrate on theconveying route is more accurately determined by comparison between thetwo sub-images, that is, M*N elements of the two sub-matrices should atleast respectively include grayscale values of pixels of the image ofthe path portion that the cut substrate is to pass on the conveyingroute.

As shown in FIG. 2, on the basis of the above-described embodiments, instep S203, the performing error analysis between the first real-timesub-matrix and the first reference sub-matrix, and determining whetheror not there is the dummy substrate on the conveying route according tothe error analysis result, for example includes:

Defining a first single pixel absolute error:

ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)− H}−{G _(MN)(i, j)− G}|

where

${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$

Defining a first objective function,

${E_{1} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{1}\left( {i,j} \right)}}}};$

Setting a first objective function threshold K¹, and comparing the firstobjective function with the first objective function threshold K¹;

Determining that there is the dummy substrate on the conveying route, ifthe first objective function is greater than the above-describedthreshold K¹;

Determining that there is no dummy substrate on the conveying route, ifthe first objective function is not greater than the above-describedthreshold K¹.

As shown in FIG. 1, on the basis of the above-described respectivelyembodiments, the dummy substrate detection method provided by theembodiments of the present disclosure for example further comprise stepsof:

Step S103: acquiring a real-time image during a process of the cutsubstrate passing through the conveying route;

Step S104: matching and comparing the acquired real-time image duringthe process of the cut substrate passing through the conveying routewith a second reference image, and determining whether or not there isthe dummy substrate on the cut substrate according to a comparisonresult; in which, the second reference image is an image during theprocess of the cut substrate with no dummy substrate passing through theconveying route.

In the dummy substrate detection method provided by the embodiments ofthe present disclosure, in step S103 to step S104, the image during theprocess of the cut substrate with no dummy substrate passing through theconveying route is taken as the second reference image, and thereal-time image during the process of the cut substrate passing throughthe conveying route is matched and compared with the above-describedsecond reference image, which accurately and effectively determineswhether or not there is the dummy substrate on the cut substrate, so asto achieve the detection of the dummy substrate on the cut substrate,and further, avoids the cut substrate carrying the dummy substrate frombeing conveyed to a next processing procedure, causing a defect of theproduct in a production process.

Furthermore, in the dummy substrate detection method provided by theembodiments of the present disclosure, the dummy substrate dropped inthe conveying route is detected by step S101 to step S102, and the dummysubstrate carried by the cut substrate is detected by step S103 to stepS104, and thus, the dummy substrate detection method provided by theembodiments of the present disclosure effectively avoid problems such asthat the cut substrate is scratched by the dummy substrate in theconveying route, or the output substrate is defective due to the dummysubstrate carried by the output substrate.

As shown in FIG. 1 and FIG. 3, further, a same processing mode forexample is used in step S104 as step S102; and step S104, matching andcomparing the acquired real-time image during the process of the cutsubstrate passing through the conveying route with the second referenceimage, and determining whether or not there is the dummy substrate onthe cut substrate according to the comparison result, includes steps of:

Step S301:

Performing grayscale processing on the second reference image, togenerate a pixel grayscale value matrix of the second reference image:

$G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the second reference image; and G²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second referenceimage;

Defining the acquired real-time image of the cut substrate passingthrough the conveying route as a second real-time image, and performinggrayscale processing on the second real-time image, to generate a pixelgrayscale value matrix of the second real-time image:

$H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the second real-time image; and H²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second real-timeimage;

Step S302:

Intercepting an E*F sub-matrix G_(EF) from the pixel grayscale valuematrix of the second reference image, and defining the sub-matrix as asecond reference sub-matrix;

Obtaining an E*F sub-matrix matching the second reference sub-matrixfrom the pixel grayscale value matrix of the second real-time image, anddefining the sub-matrix as a second real-time sub-matrix;

Step S303:

Performing error analysis between the second real-time sub-matrix andthe second reference sub-matrix, and determining whether or not there isthe dummy substrate on the cut substrate according to an error analysisresult.

In the dummy substrate detection method provided by the embodiments ofthe present disclosure, in step S104, the second reference sub-matrixand the second real-time sub-matrix are obtained by matching processing,and error analysis is performed between the second reference sub-matrixand the second real-time sub-matrix, to determine whether or not thereis the dummy substrate on the cut substrate; and the method avoidserroneous determination caused by interference of factors such asenvironment and mechanical stability.

As shown in FIG. 2 and FIG. 3, on the basis of the above-describedembodiments, processing modes of step S302 and step S303 for example arerespectively the same as those of step S202 and step S203;

For example, in step S302, the obtaining the E*F sub-matrix matching thesecond reference sub-matrix from the pixel grayscale value matrix of thesecond real-time image, is implemented in a mode below:

Setting an E*F sub-matrix H_(EF) ^(e,f) intercepted with an e-th row andan f-th column as starting position from the pixel grayscale valuematrix of the second real-time image as the E*F sub-matrix matching thesecond reference sub-matrix G_(EF), where, 1≤e+E≤x, 1≤f+F≤y;

Defining a second matching function:

${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

Where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) is an element of ani-th row and a j-th column in the matrix G_(EF);

Defining a second correlation function:

${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$

Calculating numerical values of e and f when the second correlationfunction R_((e,f)) is closest to 1, and obtaining H_(EF) ^(e,f), thatis, obtaining the E*F sub-matrix matching the second referencesub-matrix, according to the numerical values of e and f.

The above-described processing mode, that is, two sub-matrices (i.e.,G_(EF) and H_(EF) ^(e,f)) whose correlation degree is closest to 1 arerespectively extracted from the pixel grayscale value matrix of thesecond reference image and the pixel grayscale value matrix of thesecond real-time image, that is, two sub-images matching each other arerespectively extracted from the second reference image and the secondreal-time image, and the two sub-images are compared to determinewhether or not there is the dummy substrate on the cut substrate, whichavoids erroneous determination caused by interference of factors such asenvironment and mechanical stability. For example, it is required thateach sub-image in the two sub-images at least respectively includespixels covering the entire cut substrate, to ensure that whether or notthere is the dummy substrate on the cut substrate is determined bycomparison of the two sub-images, that is, E*F elements of eachsub-matrix in the two sub-matrices should at least respectively includegrayscale values of pixels covering an image of the entire cutsubstrate.

Further, step S303, performing error analysis between the secondreal-time sub-matrix and the second reference sub-matrix, anddetermining whether or not there is the dummy substrate on the cutsubstrate according to an error analysis result, for example includes:

Defining a second single pixel absolute error:

ε²(i, j)=|{H _(EF) ^(e,f)(i, j)− H}−{G _(EF)(i, j)− G}|

Where,

${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};{\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$

Defining a second objective function,

${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$

Setting a second objective function threshold K², and comparing thesecond objective function with the second objective function thresholdK²;

Determining that there is the dummy substrate on the cut substrate, ifthe second objective function is greater than the above-describedthreshold K²;

Determining that there is no dummy substrate on the cut substrate, ifthe second objective function is not greater than the above-describedthreshold K².

It should be noted that, since x, y, M, N, m, n, E, F, e, f, as well asi and j as descried above all correspond to row numbers or columnnumbers in the matrix, their values are all positive integers.

Based on the dummy substrate detection method according to theembodiments of the present disclosure, the embodiments of the presentdisclosure further provide a dummy substrate detector device. As shownin FIG. 4, the dummy Substrate detector device 1 comprises:

An image acquisition component 2, configured to acquire a real-timeimage of a conveying route when a cut substrate enters the conveyingroute, and save the image as a first real-time image;

An image processing component 3, configured to match and compare thefirst real-time image acquired by the image acquisition component 2 witha first reference image, and determine whether or not there is a dummysubstrate on the conveying route according to a comparison result; inwhich, the first reference image is an image of the conveying route withno dummy substrate.

As shown in FIG. 5, the image processing component 3 for exampleincludes a matrix processor element 31, a matrix matcher element 32 andan analyzer element 33, in which:

The matrix processor element 31 for example performs grayscaleprocessing respectively on the first reference image and the firstreal-time image, to generate a pixel grayscale value matrix G_(xy) ¹ ofthe first reference image according to the first reference imagesubjected to grayscale processing, and generate a pixel grayscale valuematrix H_(xy) ¹ of the first real-time image according to the firstreal-time image subjected to grayscale processing, for example, asfollows:

$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the first reference image; and G¹(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the first reference image;

$H_{xy}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the first real-time image; and H¹(x, y) is a grayscale value ofthe pixel located in an x-th row and a y-th column in the firstreal-time image;

The matrix matcher element 32 for example intercepts an M*N sub-matrix(i.e., a first reference sub-matrix) from the pixel grayscale valuematrix of the first reference image, and obtain an M*N sub-matrix (i.e.,a first real-time sub-matrix) matching the first reference sub-matrixfrom the pixel grayscale value matrix of the first real-time image;

The analyzer element 33 for example performs error analysis between thefirst real-time sub-matrix and the first reference sub-matrix, anddetermines whether or not there is the dummy substrate on the conveyingroute according to an error analysis result.

As shown in FIG. 5, on the basis of the above-described embodiments, thematrix matcher element 32 for example implements obtaining the firstreal-time sub-matrix from the pixel grayscale value matrix of the firstreal-time image in a mode below:

Setting an M*N sub-matrix H_(MN) ^(m,n) intercepted with an m-th row andan n-th column as starting position from the pixel grayscale valuematrix of the first real-time image as the M*N sub-matrix matching thefirst reference sub-matrix G_(MN), where, 1≤m+M≤x, 1≤n+N≤y;

Defining a first matching function:

$D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}$

Where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is an element of ani-th row and a j-th column in the matrix G_(MN);

Defining a first correlation function:

${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$

Calculating numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtaining H_(MN) ^(m,n)according to the numerical values of m and n, that is, obtaining the M*Nsub-matrix matching the first reference sub-matrix.

As shown in FIG. 5, on the basis of the above-described embodiments, theanalyzer element 33 for example implements performing error analysisbetween the first real-time sub-matrix and the first referencesub-matrix, and outputting the determination result of whether or notthere is the dummy substrate on the conveying route in a processing modebelow:

Defining a first single pixel absolute error:

ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)− H}−{G _(MN)(i, j)− G}|

Where,

${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$

Defining a first objective function,

${E_{1} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{1}\left( {i,j} \right)}}}};$

Setting a first objective function threshold K¹; comparing the firstobjective function with the first objective function threshold K¹, andoutputting a determination result.

The determination result is that there is the dummy substrate on theconveying route, if the first objective function is greater than theabove-described threshold K¹;

The determination result is that there is no dummy substrate on theconveying route, if the first objective function is not greater than thefirst objective function threshold K¹.

As shown in FIG. 4, on the basis of the above-described respectivelyembodiments, the dummy substrate detector device 1 provided by theembodiments of the present disclosure for example further is used fordetecting whether or not there is the dummy substrate on the cutsubstrate; for example, in the dummy substrate detector device providedby the embodiments of the present disclosure:

The image acquisition component 2 for example is further configured toacquire a real-time image during a process of the cut substrate passingthrough the conveying route, and save the image as a second real-timeimage;

Correspondingly, the image processing component 3 for example further isconfigured to match and compare the second real-time image with a secondreference image, and determine whether or not there is the dummysubstrate on the cut substrate according to the comparison result; inwhich, the second reference image is an image during a process of thecut substrate with no dummy substrate passing through the conveyingroute.

As shown in FIG. 5, on the basis of the above-described embodiments, inthe image processing component 3 provided by the embodiments of thepresent disclosure:

The matrix processor element 31 for example respectively performgrayscale processing on the second reference image and the secondreal-time image, to generate a pixel grayscale value matrix G_(xy) ² ofthe second reference image according to the second reference imagesubjected to grayscale processing, and generate a pixel grayscale valuematrix H_(xy) ² of the second real-time image according to the secondreal-time image subjected to grayscale processing, for example:

$G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the second reference image; and G²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second referenceimage;

The second real-time image is subjected to grayscale processing, togenerate the pixel grayscale value matrix of the second real-time image:

$H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$

Where, x and y are respectively a row number and a column number of apixel in the second real-time image; and H²(x,y) is a grayscale value ofthe pixel in an x-th row and a y-th column in the second real-timeimage;

The matrix matcher element 32 for example intercepts an E*F sub-matrix(i.e., a second reference sub-matrix) from the pixel grayscale valuematrix of the second reference image; and obtain an E*F sub-matrix(i.e., a second real-time sub-matrix) matching the second referencesub-matrix from the pixel grayscale value matrix of the second real-timeimage;

The analyzer element 33 for example performs error analysis between thesecond real-time sub-matrix and the second reference sub-matrix, anddetermine whether or not there is the dummy substrate on the cutsubstrate according to an error analysis result.

As shown in FIG. 5, on the basis of the above-described embodiments, thematrix matcher element 32 for example implements obtaining the secondreal-time sub-matrix from the pixel grayscale value matrix of the secondreal-time image in a processing mode below:

Setting the E*F sub-matrix H_(G) _(EF) ^(e,f) intercepted with an e-throw and an f-th column as starting position from the pixel grayscalevalue matrix of the second real-time image as the E*F sub-matrixmatching the second reference sub-matrix G_(EF), where, 1≤e+E≤x,1≤f+F≤y;

Defining a second matching function:

${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$

Where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) is an element of ani-th row and a j-th column in the matrix G_(EF);

Defining a second correlation function:

${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$

Calculating numerical values of e and f when the second correlationfunction R_((e,f)) is closest to 1, and obtaining H_(EF) ^(e,f), thatis, obtaining the E*F sub-matrix matching the second referencesub-matrix, according to the numerical values of e and f.

As shown in FIG. 5, on the basis of the above-described embodiments, theanalyzer element 33 for example implements performing error analysisbetween the second real-time sub-matrix and the second referencesub-matrix, and outputting the determination result of whether or notthere is the dummy substrate on the cut substrate in a processing modebelow:

Defining a second single pixel absolute error:

ε²(i, j)=|{H _(EF) ^(e,f)(i, j)− H}−{G _(EF)(i, j)− G}|

Where,

${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};$${\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$

Defining a second objective function,

${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$

Setting a second objective function threshold K²; comparing the secondobjective function with the second objective function threshold K², andoutputting a determination result;

The determination result is that there is the dummy substrate on the cutsubstrate, if the second objective function is greater than the secondobjective function threshold K²;

The determination result is that there is no dummy substrate on the cutsubstrate, if the second objective function is not greater than thesecond objective function threshold K².

For example, in the dummy substrate detector device 1 provided by theembodiments of the present disclosure, as shown in FIG. 6, the imageacquisition component 2 for example includes an image sensor 21, and forexample further includes a servo driver 22 for driving the image sensor21 to acquire an image by scanning; and as shown in FIG. 5, the imageprocessing component 3 for example is a hardware having an imageprocessing function such as a computer.

The servo driver is configured for driving the image sensor to acquirethe image by scanning, so that the image acquisition is automaticallyimplements, and further stability of a position of the image acquisitionis ensured each time.

On the basis of the above-described embodiments, in the dummy substratedetector device of the embodiments of the present disclosure, the firstreference image and the second reference image are images as comparisonstandards, and therefore, they for example are acquired and stored inthe dummy substrate detector device in advance, while the firstreal-time image and the second real-time image are acquired in real timeduring the process of conveying the cut substrate; further, the pixelgrayscale value matrix of the first reference image for example isdirectly stored in the dummy substrate detector device, for directmatching and comparison with the pixel grayscale value matrix of thefirst real-time image; and similarly, the pixel grayscale value matrixof the second reference image for example is directly stored in thedummy substrate detector device, for direct matching and comparison withthe pixel grayscale value matrix of the second real-time image.

Further, the second real-time image according to the above-describedembodiments for example is a real-time image during the process of thecut substrate passing through a set position of the conveying route;correspondingly, the second reference image is an image during theprocess of the cut substrate with no dummy passing through the setposition of the conveying route; further, in a case where the secondreal-time image is acquired, a shot of the image acquisition componentis fixed at the set position of the conveying route, and when the cutsubstrate is conveyed through the set position, the image acquisitioncomponent obtains the image during the process of the cut substratepassing through the set position of the conveying route, that is, obtainthe second real-time image.

Based on the dummy substrate detection method provided by theembodiments of the present disclosure, the embodiments of the presentdisclosure further provide a dummy substrate detector system, and asshown in FIG. 4, the dummy substrate detector system comprises the dummysubstrate detector device 1 according to any one of the above-describedembodiments.

As shown in FIG. 4 and FIG. 7 to FIG. 8, the dummy substrate detectorsystem provided by the embodiments of the present disclosure furthercomprises: a conveyor device 5, for conveying the cut substrate 4 sothat the cut substrate 4 passes through the conveying route; and adriver device 6, for driving the conveyor device 5 to move; for example,the conveyor device 5 is a roller conveyor 50, or a conveyor device ofother type such as a conveyor belt; a conveying path of the conveyordevice 5 passes through the conveying route; and the driver device 6 forexample is a driving motor, or other driver device such as a steppermotor.

As shown in FIG. 4 and FIG. 7 to FIG. 8, on the basis of theabove-described embodiments, the dummy substrate detector systemaccording to the embodiments of the present disclosure for examplefurther includes a first sensor 8 and a main controller device 7.

For example, the first sensor 8 senses the cut substrate 4, andgenerates a first sensing signal, and the first sensing signal is usedas a trigger signal for the dummy substrate detector device 1 to startdetecting whether or not there is the dummy substrate on the conveyingroute; for example, the first sensor 8 is mounted at a starting end ofthe conveying route, and further, when the cut substrate 4 is conveyedto the starting end of the conveying route, the first sensor 8 sensesthe cut substrate 4 and generates the first sensing signal.

The main controller device 7 is in signal connection with the driverdevice 6, the first sensor 8, as well as the image acquisition component2 and the image processing component 3 in the dummy substrate detectordevice 1.

On the basis of the above-described embodiments, in a case where thefirst reference image or the pixel grayscale value matrix of the firstreference image is directly pre-stored in the dummy substrate detectordevice, as shown in FIG. 4 to FIG. 9, the dummy substrate detectorsystem provided by the embodiments of the present disclosure is used fordetecting whether or not there is the dummy substrate on the conveyingroute, and steps below for example are included:

Step S401: conveying, by the conveyor 50, the cut substrate 4 to thestarting end of the conveying route, and generating the first sensingsignal by the first sensor 8;

Step S402: controlling, by the main controller device 7, the driverdevice 6 to stop driving the conveyor 50, that is, stop conveying thecut substrate 4, according to the first sensing signal of the firstsensor 8; and controlling the image acquisition component 2 to startacquiring the real-time image (the first real-time image) of theconveying route, that is, starting detection whether or not there is thedummy substrate on the conveying route by the dummy substrate detectordevice 1;

Step S403: determining, by the image processing component 3, whether ornot there is the dummy substrate on the conveying route, according tothe first reference image or the pixel grayscale value matrix of thefirst reference image pre-stored in the dummy substrate detector device1, as well as the above-described first real-time image;

Step S404: controlling, by the main controller device 7, the driverdevice 6 to drive the conveyor 50 in a case where the image processingcomponent 3 determines that there is no dummy substrate on the conveyingroute, so as to continue to convey the cut substrate 4, so that the cutsubstrate 4 passes through the conveying route;

Step S405: controlling, by the main controller device 7, generation ofan alarm signal in a case where the image processing component 3determines that there is the dummy substrate on the conveying route, soas to remind a worker to clear the dummy substrate on the conveyingroute.

In summary, the dummy substrate detector system according to theembodiments of the present disclosure triggers the dummy substratedetector device 1 to perform the detection when each cut substrate 4reaches the starting end of the conveying route, so as to determinewhether or not there is the dummy substrate on the conveying route; inaddition, only in a case where the dummy substrate detector device 1determines that there is no dummy substrate on the conveying route, thecut substrate 4 is conveyed through the conveying route, and therefore,the dummy substrate detector system provided by the embodiments of thepresent disclosure effectively prevents the cut substrate 4 from beingscratched by the dummy substrate dropped by a previous cut substrate onthe way of the conveying route.

For example, in a case where the first reference image or the pixelgrayscale value matrix of the first reference image is not pre-stored inthe dummy substrate detector device 1, as shown in FIG. 10, the dummysubstrate detector system provided by the embodiments of the presentdisclosure is used for detecting whether or not there is the dummysubstrate on the conveying route, and steps below for example areincluded:

Step S501: starting a detection function for detecting the dummysubstrate on the conveying route;

Step S502: automatically determining, by the system, whether or not thedetection function is started for the first time; if it is started forthe first time, firstly executing step S601 to step S603, and thenrestarting execution from step S501; otherwise, directly executing stepS503;

Step S601: performing manual inspection, to ensure that there is nodummy substrate on the conveying route;

Step S602: driving, by the servo driver, a CCD to move from the startingend of the conveying route to a terminating end of the conveying route,so as to obtain the first reference image;

Step S603: transmitting, by the CCD, the first reference image to thematrix processor element, so that the matrix processor element performsgrayscale processing on the first reference image, and generates thepixel grayscale value matrix of the first reference image;

Step S503: the conveyor device stopping moving, and driving, by theservo driver, the CCD to move from the starting end of the conveyingroute to the terminating end of the conveying route, so as to obtain thefirst real-time image;

Step S504: transmitting, by the CCD, the first real-time image to thematrix processor element, so that the matrix processor element performsgrayscale processing on the first real-time image, and generates thepixel grayscale value matrix of the first real-time image;

Step S505: intercepting, by the matrix matcher element, two sub-matricesthat match each other from the pixel grayscale value matrix of the firstreal-time image and the pixel grayscale value matrix of the firstreference image;

Step S506: calculating, by the analyzer element, a first objectivefunction E1 according to the two sub-matrices;

Step S507: comparing, by the analyzer element, the first objectivefunction E1 with the first objective function threshold K1; if E1<K1,executing step S508; and if E1>K1, executing step S509;

Step S508: the conveyor device restarting moving so as to continue toconvey the cut substrate 4, so that the cut substrate 4 passes throughthe conveying route;

Step S509: outputting the alarm signal.

As shown in FIG. 4 to FIG. 8, on the basis of the above-describedembodiments, the dummy substrate detector system provided by theembodiments of the present disclosure for example further comprises asecond sensor 9 in signal connection with the main controller device 7,the second sensor 9 for example senses the cut substrate 4, and generatea second sensing signal, and the second sensing signal is used as atrigger signal to start detecting whether or not there is the dummysubstrate on the cut substrate 4.

On the basis of the above-described embodiments, in a case where thesecond reference image or the pixel grayscale value matrix of the secondreference image is directly pre-stored in the dummy substrate detectordevice, as shown in FIG. 4 to FIG. 8, and FIG. 11, the dummy substratedetector system provided by the embodiments of the present disclosure isused for detecting whether or not there is the dummy substrate on thecut substrate, and steps below for example are included:

Step S701: conveying, by the conveyor 50, the cut substrate 4 to aposition where the second sensor 9 is located, so as to trigger thesecond sensor 9 to generate the second sensing signal;

Step S702: controlling, by the main controller device 7, the imageacquisition component 2 to start acquiring the real-time image (thesecond real-time image) during the process of the cut substrate 4passing through the conveying route according to the second sensingsignal, that is, starting detection of whether or not there is the dummysubstrate on the cut substrate 4 by the dummy substrate detector device1;

Step S703: determining, by the image processing component 3, whether ornot there is the dummy substrate on the cut substrate, according to thesecond reference image or the pixel grayscale value matrix of the secondreference image pre-stored in the dummy substrate detector device 1, aswell as the above-described second real-time image;

Step S704: controlling, by the main controller device 7, the driverdevice 6 to continue to drive the conveyor 50, in a case where the imageprocessing component 3 determines that there is no dummy substrate onthe cut substrate 4;

Step S705: controlling, by the main controller device 7, generation ofan alarm signal, in a case where the image processing component 3determines that there is the dummy substrate on the cut substrate 4, soas to remind a worker to clear the dummy substrate on the cut substrate4, or controlling the conveyor 50 to stop conveying the substrate 4.

In summary, in the dummy substrate detector system provided by theembodiments of the present disclosure, when the substrate 4 triggers thesecond sensor 9, the dummy substrate detector device 1 is triggered todetect whether or not there is the dummy substrate on the cut substrate4; in addition, only in a case where the dummy substrate detector device1 determines that there is the dummy substrate on the cut substrate 4,the alarm signal is generated to remind the worker to clear the dummysubstrate on the cut substrate 4, and thus, the dummy substrate detectorsystem provided by the embodiments of the present disclosure effectivelyprevents the dummy substrate from being conveyed to a next processingprocedure, causing the cut substrate to be defective.

For example, in a case where the second reference image or the pixelgrayscale value matrix of the second reference image is not pre-storedin the dummy substrate detector device 1, as shown in FIG. 10, the dummysubstrate detector system provided by the embodiments of the presentdisclosure is used for detecting whether or not there is the dummysubstrate on the base substrate, steps below for example are included:

Step S801: starting a detection function for detecting the dummysubstrate on the cut substrate;

Step S802: automatically determining, by the system, whether or not thedetection function is started for the first time; if it is started forthe first time, executing step S901 to step S903, and then restartingexecution from step S801; otherwise, directly executing step S803;

Step S901: performing manual inspection, to ensure that there is nodummy substrate on the cut substrate;

Step S902: driving, by the conveyor device, the cut substrate to passthrough under the CCD, so that the CCD acquires the second referenceimage;

Step S903: transmitting, by the CCD, the second reference image to thematrix processor element, so that the matrix processor element performsgrayscale processing on the second reference image, and generates thepixel grayscale value matrix of the second reference image;

Step S803: driving, by the conveyor device, the cut substrate to passthrough under the CCD, so that the CCD acquires the second real-timeimage;

Step S804: transmitting, by the CCD, the acquired second real-time imageto the matrix processor element, so that the matrix processor elementperforms grayscale processing on the second real-time image, andgenerates the pixel grayscale value matrix of the second real-timeimage;

Step S805: intercepting, by the matrix matcher element, two sub-matricesthat match each other from the pixel grayscale value matrix of thesecond real-time image and the pixel grayscale value matrix of thesecond reference image;

Step S806: calculating, by the analyzer element, a second objectivefunction E2 according to the two sub-matrices;

Step S807: comparing, by the analyzer element, the second objectivefunction E2 with the second objective function threshold K2; if E2<K2,executing step S808; and if E2>K2, executing step S809;

Step S808: the conveyor continuing to move, and conveying the cutsubstrate 4 to pass through the conveying route to a next procedure;

Step S809: outputting the alarm signal.

As shown in FIG. 4 to FIG. 6, in the dummy substrate detector systemprovided by the embodiments of the present disclosure, the first sensor8 and the second sensor 9 for example are photoelectric sensors, forexample, infrared sensors; and the main controller device 7 for exampleis implemented with a computer or hardware such as a programmable logiccontroller (PLC).

As shown in FIG. 6 to FIG. 8, on the basis of the above-describedembodiments, in the dummy substrate detector system provided by theembodiments of the present disclosure, the image sensor (i.e. CCD) 21 inthe image acquisition component 2 for example is a one-dimensionallinear CCD 211 mounted on the conveying route; further, the servo driver22 for driving the one-dimensional linear CCD 211 to acquire the imageby scanning for example includes a servo motor 221 and a servo rail 222provided along the conveyor 50, and the one-dimensional linear CCD 211is mounted on the servo rail 222, and is movable along the servo rail222 to acquire the image by scanning.

As shown in FIG. 7 and FIG. 8, on the basis of the above-describedembodiments, the second sensor 9, for example, is mounted at theterminating end of the conveying route.

In the dummy substrate detector system provided by the embodiments ofthe present disclosure, the dummy substrate detector device 1 detectswhether or not there is the dummy substrate on the conveying route, andthe one-dimensional linear CCD 211, as driven by the servo driver 22,moves from the starting end to the terminating end of the conveyingroute, so as to implement acquiring the real-time image (first real-timeimage) of the conveying route; then, the cut substrate 4 reaches theterminating end of the conveying route and triggers the, second sensor 9so as to further trigger the dummy substrate detector device 1 to startdetecting whether or not there is the dummy substrate on the cutsubstrate 4, the one-dimensional linear CCD 211 just stops at theterminating end of the conveying route, and at this time, since the cutsubstrate 4 just begins to pass the terminating end of the conveyingroute, the one-dimensional linear CCD 211, without moving, obtains theprocess image (the second real-time image) of the cut substrate 4 movingalong the conveying route.

In addition, for example, after the one-dimensional linear CCD 211completes acquisition of the first real-time image and the secondreal-time image, it, as driven by the servo driver 22, returns to thestarting end of the conveying route to prepare for a next round ofdetection of the dummy substrate.

It should be noted that, the dummy substrate detector system provided bythe embodiments of the present disclosure may firstly detect whether ornot there is the dummy substrate on the cut substrate, and then detectwhether or not there is the dummy substrate on the conveying route; inthis case, it is necessary to place the second sensor ahead of thestarting end of the conveying route, and the second reference image andthe second real-time image acquired are correspondingly images of thecut substrate on a conveying path ahead of the conveying route.

In addition, in the dummy substrate detector system provided by theembodiments of the present disclosure, the cut substrate passing throughthe conveying route for example is a small piece of substrate obtainedafter cutting a large piece of mother substrate, or a cut liquid crystaldisplay panel which has been cut; in addition, the above-described smallpiece of substrate for example is made of various materials such asglass, plastic or acrylic.

As compared with a conventional pin torque detecting method and thevacuum adsorption detecting method, the dummy substrate detection methodas well as the dummy substrate detector device and the dummy substratedetector system provided by the embodiments of the present disclosurenot only effectively detects the dummy substrate on the conveying routethat cannot be detected by using the conventional method, but alsoeffectively detects the dummy substrate carried on a surface of the cutsubstrate that cannot be detected by using the conventional method; inaddition, the dummy substrate detection method provided by theembodiments of the present disclosure does not have any risk of causingphysical damage to the cut substrate; the detection result is accurateand the detection process is safe and reliable.

Several points below need to be explained:

(1) The drawings of the embodiments of the present disclosure relateonly to the structures involved in the embodiments of the presentdisclosure, and normal designs may be referred to for other structures.

(2) For the sake of clarity, in the drawings used for describing theembodiments of the present disclosure, thicknesses of layers or regionsare enlarged or reduced, that is, these drawings are not drawn in anactual scale. It may be understood that, when an element such as alayer, a film, a region or a substrate is referred to as being located“on” or “below” another element, the element may be “immediately”located “on” or “below” another element, or there may be an intermediateelement.

(3) In case of no conflict, the embodiments of the present disclosureand the features in the embodiments may be combined with each other toobtain a new embodiment.

The foregoing embodiments merely are exemplary embodiments of thedisclosure, and not intended to define the scope of the disclosure, andthe scope of the disclosure is determined by the appended claims.

1. A dummy substrate detection method, comprising: acquiring a real-timeimage of a conveying route when a cut substrate enters the conveyingroute; matching and comparing the acquired real-time image of theconveying route with a first reference image, and determining whether ornot there is a dummy substrate on the conveying route according to acomparison result; wherein, the first reference image is an image of theconveying route with no dummy substrate.
 2. The dummy substratedetection method according to claim 1, wherein, the matching andcomparing the acquired real-time image of the conveying route with thefirst reference image, and determining whether or not there is the dummysubstrate on the conveying route according to the comparison result,includes: performing grayscale processing on the first reference image,to generate a pixel grayscale value matrix of the first reference image:$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the first reference image; and G¹(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thefirst reference image; defining the acquired real-time image of theconveying route as a first real-time image, and performing grayscaleprocessing on the first real-time image, to generate a pixel grayscalevalue matrix of the first real-time image: $H_{xy}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the first real-time image; and H¹(x, y) is agrayscale value of the pixel in an x-th row and a y-th column in thefirst real-time image; intercepting an M*N sub-matrix G_(MN) from thepixel grayscale value matrix of the first reference image, and definingthe sub-matrix as a first reference sub-matrix; obtaining an M*Nsub-matrix matching the first reference sub-matrix from the pixelgrayscale value matrix of the first real-time image, and defining thesub-matrix as a first real-time sub-matrix; and performing erroranalysis between the first real-time sub-matrix and the first referencesub-matrix, and determining whether or not there is the dummy substrateon the conveying route according to an error analysis result,
 3. Thedummy substrate detection method according to claim 2, wherein, theobtaining the M*N sub-matrix matching the first reference sub-matrixfrom the pixel grayscale value matrix of the first real-time image,includes: setting an M*N sub-matrix H_(MN) ^(m,n) intercepted with anm-th row and an n-th column as starting position from the pixelgrayscale value matrix of the first real-time image as the M*Nsub-matrix matching the first reference sub-matrix G_(MN), where,1≤m+M≤x, 1≤n+N≤y; defining a first matching function:$D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}$where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is en element of ani-th row and a j-th column in the matrix G_(MN); defining a firstcorrelation function;${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$calculating numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtaining the M*N sub-matrixH_(MN) ^(m,n) matching the first reference sub-matrix according to thenumerical values of m and n.
 4. The dummy substrate detection methodaccording to claim 2, wherein, the performing error analysis between thefirst real-time sub-matrix and the first reference sub-matrix, anddetermining whether or not there is the dummy substrate on the conveyingroute according to the error analysis result, includes: defining a firstsingle pixel absolute error:ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)− H}−{G _(MN)(i, j)− G}| where,${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$defining a first objective function,${E_{i} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{1}\left( {i,j} \right)}}}};$setting a first objective function threshold K¹, and comparing the firstobjective function with the first objective function threshold K¹;determining that there is the dummy substrate on the conveying route, ifthe first objective function is greater than the first objectivefunction threshold K¹; determining that there is no dummy substrate onthe conveying route, if the first objective function is not greater thanthe first objective function threshold K¹.
 5. The dummy substratedetection method according to claim 1, further comprising: acquiring areal-time image during a process of the cut substrate passing throughthe conveying route; matching and comparing the acquired real-time imageduring the process of the cut substrate passing through the conveyingroute with a second reference image, and determining whether or notthere is the dummy substrate on the cut substrate according to acomparison result; wherein, the second reference image is an imageduring a process of the cut substrate with no dummy substrate passingthrough the conveying route.
 6. The dummy substrate detection methodaccording to claim 5, wherein, the matching and comparing the acquiredreal-time image during the process of the cut substrate passing throughthe conveying route with the second reference image, and determiningwhether or not there is the dummy substrate on the cut substrateaccording to the comparison result, includes steps of: performinggrayscale processing on the second reference image, to generate a pixelgrayscale value matrix of the second reference image:$G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the second reference image; and G²(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thesecond reference image; defining the acquired real-time image during thecut substrate passing through the conveying route as a second real-timeimage, and performing grayscale processing on the second real-timeimage, to generate a pixel grayscale value matrix of the secondreal-time image: $H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the second real-time image; and H²(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thesecond real-time image; intercepting an E*F sub-matrix G_(EF) from thepixel grayscale value matrix of the second reference image, and definingthe sub-matrix as a second reference sub-matrix; obtaining an E*Fsub-matrix matching the second reference sub-matrix from the pixelgrayscale value matrix of the second real-time image, and defining thesub-matrix as a second real-time sub-matrix; performing error analysisbetween the second real-time sub-matrix and the second referencesub-matrix, and determining whether or not there is the dummy substrateon the cut substrate according to an error analysis result.
 7. The dummysubstrate detection method according to claim 6, wherein, the obtainingthe E*F sub-matrix matching the second reference sub-matrix from thepixel grayscale value matrix of the second real-time image, includes:setting an E*F sub-matrix H_(EF) ^(e,f) intercepted with an e-th row andan f-th column as starting position from the pixel grayscale valuematrix of the second real-time image as the E*F sub-matrix matching thesecond reference sub-matrix, where, 1≤e+E≤x, 1≤f+F≤y; defining a secondmatching function:${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,j}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) is an element of ani-th row and a j-th column in the matrix G_(EF); defining a secondcorrelation function:${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$calculating numerical values of e and f when the second correlationfunction R_((e,f)) is closest to 1, and obtaining the sub-matrix H_(EF)^(e,f) matching the second reference sub-matrix, according to thenumerical values of e and f.
 8. The dummy substrate detection methodaccording to claim 6, wherein, the performing error analysis between thesecond real-time sub-matrix and the second reference sub-matrix, anddetermining whether or not there is the dummy substrate on the cutsubstrate according to the error analysis result, includes: defining asecond single pixel absolute error:ε²(i, j)=|{H _(EF) ^(e,f)(i, j)−H}−{G_(EF)(i, j)−G}|; where,${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};$${\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$defining a second objective function,${E_{2} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{ɛ^{2}\left( {i,j} \right)}}}};$setting a second objective function threshold K², and comparing thesecond objective function with the second objective function thresholdK²; determining that there is the dummy substrate on the cut substrate,if the second objective function is greater than the second objectivefunction threshold K²; determining that there is no dummy substrate onthe cut substrate, if the second objective function is not greater thanthe second objective function threshold K².
 9. A dummy substratedetector device, comprising: an image acquisition component, configuredto acquire a real-time image of a conveying route when a cut substrateenters the conveying route, and save the image as a first real-timeimage; an image processing component, configured to match and comparethe first real-time image with a first reference image, and determinewhether or not there is a dummy substrate on the conveying routeaccording to a comparison result; wherein, the first reference image isan image of the conveying route with no dummy substrate.
 10. The dummysubstrate detector device according to claim 9, wherein, the imageprocessing component includes: a matrix processor element, configuredto: perform grayscale processing on the first reference image, togenerate a pixel grayscale value matrix of the first reference image:$G_{xy}^{1} = \begin{bmatrix}{G^{1}\left( {1,1} \right)} & {G^{1}\left( {1,2} \right)} & \ldots & {G^{1}\left( {1,y} \right)} \\{G^{1}\left( {2,1} \right)} & {G^{1}\left( {2,2} \right)} & \ldots & {G^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{1}\left( {x,1} \right)} & {G^{1}\left( {x,2} \right)} & \ldots & {G^{1}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the first reference image; and G¹(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thefirst reference image; perform grayscale processing on the firstreal-time image, to generate a pixel grayscale value matrix of the firstreal-time image: $H_{xy}^{1} = \begin{bmatrix}{H^{1}\left( {1,1} \right)} & {H^{1}\left( {1,2} \right)} & \ldots & {H^{1}\left( {1,y} \right)} \\{H^{1}\left( {2,1} \right)} & {H^{1}\left( {2,2} \right)} & \ldots & {H^{1}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{1}\left( {x,1} \right)} & {H^{1}\left( {x,2} \right)} & \ldots & {H^{1}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the first real-time image; and H¹(x, y) is agrayscale value of the pixel located in an x-th row and a y-th column inthe first real-time image; a matrix matcher element, configured to:intercept an M*N sub-matrix G_(MN) from the pixel grayscale value matrixof the first reference image, and define the sub-matrix as a firstreference sub-matrix; obtain an M*N sub-matrix matching the firstreference sub-matrix from the pixel grayscale value matrix of the firstreal-time image, and define the sub-matrix as a first real-timesub-matrix; an analyzer element, configured to: perform error analysisbetween the first real-time sub-matrix and the first referencesub-matrix, and determine whether or not there is the dummy substrate onthe conveying route according to an error analysis result.
 11. The dummysubstrate detector device according to claim 10, wherein, the matrixmatcher element is configured to: set an M*N sub-matrix H_(MN) ^(m,n)intercepted with an n-th row and an nth column as starting position fromthe pixel grayscale value matrix of the first real-time image as the M*Nsub-matrix matching the first reference sub-matrix G_(MN), where,1≤m+M≤x, 1≤n+N≤y; define a first matching function:${D_{({m,n})} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {G_{MN}\left( {i,j} \right)} \right\rbrack^{2}}}}};$where, H_(MN) ^(m,n)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(MN) ^(m,n), and G_(MN)(i, j) is an element of ani-th row and a j-th column in the matrix G_(MN); define a firstcorrelation function:${R_{({m,n})} = \frac{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {{H_{MN}^{m,n}\left( {i,j} \right)} \cdot {G_{MN}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\left\lbrack {H_{MN}^{m,n}\left( {i,j} \right)} \right\rbrack^{2}}}};$calculate numerical values of m and n when the first correlationfunction R_((m,n)) is closest to 1, and obtain the M*N sub-matrix H_(MN)^(m,n) matching the first reference sub-matrix according to thenumerical values of m and n; the analyzer element, configured to: definea first single pixel absolute error:ε¹(i, j)=|{H _(MN) ^(m,n)(i, j)−H}−{G_(MN)(i, j)−G}|; where,${\overset{\_}{H} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{H_{MN}^{m,n}\left( {i,j} \right)}}}}},{{\overset{\_}{G} = {\frac{1}{M \cdot N}{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{G_{MN}\left( {i,j} \right)}}}}};}$define a first objective function,${E_{1} = {\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}\; {ɛ^{1}\left( {i,j} \right)}}}};$set a first objective function threshold K¹, and compare the firstobjective function with the first objective function threshold K¹;determine that there is the dummy substrate on the conveying route, ifthe first objective function is greater than the first objectivefunction threshold K¹; determine that there is no dummy substrate on theconveying route, if the first objective function is not greater than thefirst objective function threshold K¹.
 12. The dummy substrate detectordevice according to claim 8, wherein, the image acquisition component isfurther configured to acquire a real-time image during a process of thecut substrate passing through the conveying route, and save the image asa second real-time image; the image processing component is furtherconfigured to match and compare the second real-time image with a secondreference image, and determine whether or not there is the dummysubstrate on the cut substrate according to a comparison result;wherein, the second reference image is an image during the process ofthe cut substrate with no dummy substrate passing through the conveyingroute.
 13. The dummy substrate detector device according to claim 12,wherein, in the image processing component, the matrix processorelement, is further configured to: perform grayscale processing on thesecond reference image, to generate a pixel grayscale value matrix ofthe second reference image: $G_{xy}^{2} = \begin{bmatrix}{G^{2}\left( {1,1} \right)} & {G^{2}\left( {1,2} \right)} & \ldots & {G^{2}\left( {1,y} \right)} \\{G^{2}\left( {2,1} \right)} & {G^{2}\left( {2,2} \right)} & \ldots & {G^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{G^{2}\left( {x,1} \right)} & {G^{2}\left( {x,2} \right)} & \ldots & {G^{2}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the second reference image; and G²(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thesecond reference image; perform grayscale processing on the secondreal-time image, to generate a pixel grayscale value matrix of thesecond real-time image: $H_{xy}^{2} = \begin{bmatrix}{H^{2}\left( {1,1} \right)} & {H^{2}\left( {1,2} \right)} & \ldots & {H^{2}\left( {1,y} \right)} \\{H^{2}\left( {2,1} \right)} & {H^{2}\left( {2,2} \right)} & \ldots & {H^{2}\left( {2,y} \right)} \\\ldots & \ldots & \ldots & \ldots \\{H^{2}\left( {x,1} \right)} & {H^{2}\left( {x,2} \right)} & \ldots & {H^{2}\left( {x,y} \right)}\end{bmatrix}$ where, x and y are respectively a row number and a columnnumber of a pixel in the second real-time image; and H²(x,y) is agrayscale value of the pixel in an x-th row and a y-th column in thesecond real-time image; the matrix matcher element is further configuredto: intercept an E*F sub-matrix G_(EF) from the pixel grayscale valuematrix of the second reference image; and define the sub-matrix as asecond reference sub-matrix; obtain an E*F sub-matrix matching thesecond reference sub-matrix from the pixel grayscale value matrix of thesecond real-time image, and define the sub-matrix as a second real-timesub-matrix; the analyzer element is further configured to: perform erroranalysis between the second real-time sub-matrix and the secondreference sub-matrix, and determine whether or not there is the dummysubstrate on the cut substrate according to an error analysis result,14. The dummy substrate detector device according to claim 13, wherein,the matrix matcher element is configured to: set the E*F sub-matrixH_(EF)^(e, f) intercepted with an e-th row and an f-th column asstarting position from the pixel grayscale value matrix of the secondreal-time image as the E*F sub-matrix matching the second referencesub-matrix G_(EF), where, 1≤e+E≤x, 1≤f+F≤y; define a second matchingfunction:${D_{({e,f})} = {{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}} - {2{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}} + {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {G_{EF}\left( {i,j} \right)} \right\rbrack^{2}}}}};$where, H_(EF) ^(e,f)(i, j) is an element of an i-th row and a j-thcolumn in the matrix H_(EF) ^(e,f), and G_(EF)(i, j) is an element of ani-th row and a j-th column in the matrix G_(EF); define a secondcorrelation function:${R_{({e,f})} = \frac{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {{H_{EF}^{e,f}\left( {i,j} \right)} \cdot {G_{EF}\left( {i,j} \right)}} \right\rbrack}}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}\left\lbrack {H_{EF}^{e,f}\left( {i,j} \right)} \right\rbrack^{2}}}};$calculate numerical values of e and f when the second correlationfunction R_((e,f)) is closest to 1, and obtain the E*F sub-matrix H_(EF)^(e,f) matching the second reference sub-matrix, according to thenumerical values of e and f: the analyzer element is configured to:define a second single pixel absolute error:ε²(i, j)=|{H _(EF) ^(e,f)(i, j)−H}−{G_(EF)(i, j)−G}|; where,${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};$${\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$define a second objective function,${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$set a second objective function threshold K², and compare the secondobjective function with the second objective function threshold K²;determine that there is the dummy substrate on the cut substrate, if thesecond objective function is greater than the second objective functionthreshold K²; determine that there is no dummy substrate on the cutsubstrate, if the second objective function is not greater than thesecond objective function threshold K².
 15. A dummy substrate detectorsystem, comprising the dummy substrate detector device according toclaim 12, and, a conveyor device, configured to convey the cut substratethrough the conveying route; a driver device, configured to drive theconveyor device to move; a first sensor, at a starting end of theconveying route, configured to sense the cut substrate and generate afirst sensing signal; a main controller device, in signal connectionwith the image acquisition component, the image processing component,the driver device and the first sensor, configured to: control thedriver device to stop driving the conveyor device, when receiving thefirst sensing signal of the first sensor, and control the imageacquisition component to acquire the real-time image of the conveyingroute; and, control the driver device to drive the conveyor device tocontinue to move, in a case where the image processing componentdetermines that there is no dummy substrate on the conveying route. 16.The dummy substrate detector system according to claim 15, furthercomprising: a second sensor in signal connection with the maincontroller device, wherein, the second sensor is in the conveying route,and is configured to sense the cut substrate and generate a sensingsignal; the main controller device, further configured to: control theimage acquisition component to acquire the real-time image during theprocess of the cut substrate sensed by the second sensor passing throughthe conveying route, when receiving the second sensing signal of thesecond sensor; and generate an alarm signal, in a case where the imageprocessing component determines that there is the dummy substrate on thecut substrate.
 17. The dummy substrate detector system according toclaim 15, wherein, the image acquisition component includes aone-dimensional linear image sensor and a servo driver configured todrive the one-dimensional linear image sensor to move so as to obtain animage.
 18. The dummy substrate detection method according to claim 7,wherein, the performing error analysis between the second real-timesub-matrix and the second reference sub-matrix, and determining whetheror not there is the dummy substrate on the cut substrate according tothe error analysis result, includes: defining a second single pixelabsolute error:ε²(i, j)=|{H _(EF) ^(e,f)(i, j)−H}−{G_(EF)(i, j)−G}|; where,${\overset{\_}{H} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{H_{EF}^{e,f}\left( {i,j} \right)}}}}};$${\overset{\_}{G} = {\frac{1}{E \cdot F}{\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{G_{EF}\left( {i,j} \right)}}}}};$defining a second objective function,${E_{2} = {\sum\limits_{i = 1}^{E}{\sum\limits_{j = 1}^{F}{ɛ^{2}\left( {i,j} \right)}}}};$setting a second objective function threshold K², and comparing thesecond objective function with the second objective function thresholdK²; determining that there is the dummy substrate on the cut substrate,if the second objective function is greater than the second objectivefunction threshold K²; determining that there is no dummy substrate onthe cut substrate, if the second objective function is not greater thanthe second objective function threshold K².
 19. The dummy substratedetector system according to claim 16, wherein, the image acquisitioncomponent includes a one-dimensional linear image sensor and a servodriver configured to drive the one-dimensional linear image sensor tomove so as to obtain an image.